The First Modeling of the Spot-Scanning Proton Arc (SPArc) Delivery Sequence and Investigating its Efficiency Improvement in the Clinical Proton Treatment Workflow

Document Type

Conference Proceeding

Publication Date


Publication Title

International Journal of Radiation Oncology, Biology, Physics



To quantitatively model a precise spot-scanning proton arc (SPArc) delivery sequence and assess its efficiency improvement in the routine proton clinical operation.


The SPArc delivery sequence model (DSM SPArc ) includes two kinds of parameters: (1) mechanical parameters (the maximum gantry velocity, acceleration, and deceleration speed). (2) irradiation parameters (tolerance window and buffer, spot scanning speed, energy layer switching time, and burst switching time). An independent gantry inclinometer was used to measure mechanical parameters. Log files were used to derive the irradiation parameters through a series of SPArc test plans. The in-house DSM SPArc was established by fitting both mechanical and irradiation parameters. Eight SPArc plans from different disease sites (brain, HN, lung, and liver cancer) were used to validate the model's accuracy. To quantitatively assess the treatment efficiency improvement compared to the clinical IMPT, a random clinical operation date of our proton center (total 21 cases on Jan 6 th 2021) was selected, and SPArc plans were generated for all the cases. The DSM SPArc was used to simulate the SPArc treatment delivery sequence and compared to the clinical IMPT treatment logfiles.


The relative difference of treatment time between log files and DSM SPArc ’s prediction was 6.1% ± 3.9% on average, and the gantry angle vs. delivery time showed a good agreement between the DSM SPArc and log file. Additionally, the SPArc plan could effectively save two hours out of 10 hours of clinical operation by simplifying the treatment workflow for a single room proton therapy center. The average treatment delivery time (including gantry rotation and irradiation) per patient was reduced to 226 ± 149s using SPArc compared to 665 ± 407s using IMPT ( P < 0.01).


This is the first modeling of the SPArc delivery sequence, which paves the roadmap for implementing the delivery speed and time into the SPArc optimization algorithm. Additionally, SPArc can offer a superior delivery efficiency to improve clinical treatment throughput, compared to IMPT.





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